for (i in 1:length(params))
print(paste('Parameter:', names(params)[i], ' - Value:', params[[i]], '- Class:', class(params[[i]])))
## [1] "Parameter: Dataset - Value: CHD2_iPSCs_and_organoids_PublicRepo - Class: character"
## [1] "Parameter: SEFile - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/iPSCs/Output/Savings/ipsc.SE_deseq2_HT.rds - Class: character"
## [1] "Parameter: DEAList - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/iPSCs/Output/Savings/ipsc.DEAList_HT.rds - Class: character"
## [1] "Parameter: HT - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/iPSCs/Output/Savings/ipsc.deseqHTvsWT.rds - Class: character"
## [1] "Parameter: SavingFolder - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/6.Enrichments/iPSCs/Output/Savings/ - Class: character"
## [1] "Parameter: FiguresFolder - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/6.Enrichments/iPSCs/Output/Figures/ - Class: character"
## [1] "Parameter: FDRthr - Value: 0.05 - Class: numeric"
## [1] "Parameter: logFCthr - Value: 0.55 - Class: numeric"
## [1] "Parameter: TopGO - Value: BP_MF_CC - Class: character"
## [1] "Parameter: GoEnTh - Value: 1 - Class: numeric"
## [1] "Parameter: GoPvalTh - Value: 0.05 - Class: numeric"
## [1] "Parameter: NbName - Value: TopGO_iPSCs_HT - Class: character"
## [1] "Parameter: SaveImages - Value: FALSE - Class: logical"library(RNASeqBulkExploratory)
library(SummarizedExperiment)
library(tidyr)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(topGO)
library(sechm)
library(ggplot2)
library(grid)
library(gridExtra)
library(RColorBrewer)
library(cowplot)
source('/group/testa/Users/oliviero.leonardi/myProjects/CHD2/BulkRNAseq/ContainerHome/CHD2_organoids/NoGradientBarplots.R')Dataset <- params$Dataset
logFCthr <- params$logFCthr
FDRthr <- params$FDRthr
FdrTh <- FDRthr
logFcTh <- logFCthr
SavingFolder <- ifelse(is.null(params$SavingFolder), getwd(), params$SavingFolder)
FiguresFolder <- ifelse(is.null(params$FiguresFolder), getwd(), params$FiguresFolder)
if (dir.exists(SavingFolder) == FALSE) {
dir.create(SavingFolder, recursive=TRUE)
}#SE object coming from DEA, but not containing specific contrast results
SE_DEA <- readRDS(params$SEFile)
SE_DEA <- SE_DEA[rowData(SE_DEA)$GeneName != '', ]
rownames(SE_DEA) <- rowData(SE_DEA)$GeneName
# List with differential expression results (all time-points)
DEA <- readRDS(params$DEAList)colvector <- c("#5ec962", "#e95462", "#2c728e")
names(colvector) <- c('All', 'Up', 'Down')if(! identical(rownames(SE_DEA), row.names(DEA$HT$res))){
stop('Expression data in SE and results from differential espression analysis are inconsistent.')
}
SE_DEA <- mergeDeaSE(SE_DEA, DEA$HT$res, subsetRowDataCols=NULL,
logFcCol='log2FoldChange', FdrCol='padj') #specify
## Renaming " log2FoldChange " to "logFC"
## Renaming " padj " to "FDR"16981 genes in 9 samples have been testes for differential expression.
The following number of genes are identified as differentially expressed:
Imposing a threshold of 0.55 on the Log2FC and 0.05 on the FDR (as specified in parameters), 3649 genes are selected: 2700 up-regulated genes and 2707 down-regulated genes.
The results of the differential expression analysis are visualized by Volcano plot. An interactive version is included in the html (only genes with FDR < threshold), while a static version is saved.
plotVolcanoSE(SE=SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr,
FdrCeil=1e-10, logFcCeil=4, Interactive = FALSE)## Warning: Removed 16781 rows containing missing values or values outside the scale range
## (`geom_text_repel()`).
## Warning: ggrepel: 113 unlabeled data points (too many overlaps). Consider increasing max.overlaps
plotVolcanoSE(SE=SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr,
FdrCeil=1e-10, logFcCeil=4, Interactive = TRUE)Heatmaps for DEGs, showing scaled vst values.
DEGs <- dplyr::filter(data.frame(rowData(SE_DEA)), FDR < FDRthr & abs(logFC) > log2(logFCthr))
ScaledCols <- c('darkblue', "purple","white","lightgoldenrod1", 'goldenrod1')# sechm::sechm(SE_DEA, genes=DEGs$GeneName, assayName="vst", gaps_at="Genotype", show_rownames=FALSE,
# top_annotation=c('Genotype'), hmcols=ScaledCols, show_colnames=TRUE,
# do.scale=TRUE, breaks=0.85, column_title = "Scaled Vst Values")Gene ontology enrichment analysis is performed on the set of 3649 genes using TopGO with Fisher statistics and weight01 algorithm.
For each specified domain of the ontology:
I generate vectors for the gene universe, all modulated genes, up-regulated genes and down-regulated genes in the format required by TopGo.
GeneVectors <- topGOGeneVectors(SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr)## Gene vector contains levels: 0,1
## Gene vector contains levels: 0,1
## Gene vector contains levels: 0,1
Therefore:
Then I set parameters according to the gene ontology domains to be evaluated. By default, Biological Process and Molecular Function domains are interrogated.
BpEval <- ifelse(length(grep('BP', params$TopGO))!=0, TRUE, FALSE)
MfEval <- ifelse(length(grep('MF', params$TopGO))!=0, TRUE, FALSE)
CcEval <- ifelse(length(grep('CC', params$TopGO))!=0, TRUE, FALSE)On the basis of the analysis settings, the enrichment for Biological Process IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
BPannHT <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$DEGenes),
mapping="org.Hs.eg.db", ID="symbol") %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResBPAllHT <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=BPannHT, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPAllHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11678 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15327 GO terms and 34854 relations. )
##
## Annotating nodes ...............
## ( 13748 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 7776 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 3 nodes to be scored (0 eliminated genes)
##
## Level 18: 9 nodes to be scored (0 eliminated genes)
##
## Level 17: 12 nodes to be scored (31 eliminated genes)
##
## Level 16: 31 nodes to be scored (54 eliminated genes)
##
## Level 15: 74 nodes to be scored (140 eliminated genes)
##
## Level 14: 142 nodes to be scored (373 eliminated genes)
##
## Level 13: 236 nodes to be scored (791 eliminated genes)
##
## Level 12: 384 nodes to be scored (1829 eliminated genes)
##
## Level 11: 688 nodes to be scored (3873 eliminated genes)
##
## Level 10: 998 nodes to be scored (5939 eliminated genes)
##
## Level 9: 1202 nodes to be scored (7698 eliminated genes)
##
## Level 8: 1172 nodes to be scored (9592 eliminated genes)
##
## Level 7: 1067 nodes to be scored (11065 eliminated genes)
##
## Level 6: 854 nodes to be scored (12166 eliminated genes)
##
## Level 5: 509 nodes to be scored (12798 eliminated genes)
##
## Level 4: 262 nodes to be scored (13276 eliminated genes)
##
## Level 3: 110 nodes to be scored (13446 eliminated genes)
##
## Level 2: 22 nodes to be scored (13536 eliminated genes)
##
## Level 1: 1 nodes to be scored (13586 eliminated genes)# Wrapper function for topGO analysis (see helper file)
ResBPDownHT <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=BPannHT, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPDownHT', outDir=paste0(SavingFolder))
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11678 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15327 GO terms and 34854 relations. )
##
## Annotating nodes ...............
## ( 13748 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 6505 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 1 nodes to be scored (0 eliminated genes)
##
## Level 18: 8 nodes to be scored (0 eliminated genes)
##
## Level 17: 11 nodes to be scored (18 eliminated genes)
##
## Level 16: 23 nodes to be scored (50 eliminated genes)
##
## Level 15: 58 nodes to be scored (136 eliminated genes)
##
## Level 14: 109 nodes to be scored (336 eliminated genes)
##
## Level 13: 165 nodes to be scored (709 eliminated genes)
##
## Level 12: 282 nodes to be scored (1657 eliminated genes)
##
## Level 11: 520 nodes to be scored (3643 eliminated genes)
##
## Level 10: 794 nodes to be scored (5779 eliminated genes)
##
## Level 9: 988 nodes to be scored (7387 eliminated genes)
##
## Level 8: 1005 nodes to be scored (9350 eliminated genes)
##
## Level 7: 934 nodes to be scored (10911 eliminated genes)
##
## Level 6: 763 nodes to be scored (12051 eliminated genes)
##
## Level 5: 470 nodes to be scored (12773 eliminated genes)
##
## Level 4: 247 nodes to be scored (13266 eliminated genes)
##
## Level 3: 104 nodes to be scored (13444 eliminated genes)
##
## Level 2: 22 nodes to be scored (13536 eliminated genes)
##
## Level 1: 1 nodes to be scored (13586 eliminated genes)
# Selection on enrichment of at least 2 is implemented (also to avoid depleted categories). Then categories are ranked by PVal and all the ones with Pval < th are selected. If the number is < minTerms, othter terms are included to reach the minimum number. GOTable(ResBPDownHT$ResSel, maxGO=20)ResBPUpHT <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=BPannHT, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPUpHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11678 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15327 GO terms and 34854 relations. )
##
## Annotating nodes ...............
## ( 13748 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 6872 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 19: 3 nodes to be scored (0 eliminated genes)
##
## Level 18: 9 nodes to be scored (0 eliminated genes)
##
## Level 17: 12 nodes to be scored (31 eliminated genes)
##
## Level 16: 26 nodes to be scored (54 eliminated genes)
##
## Level 15: 59 nodes to be scored (140 eliminated genes)
##
## Level 14: 118 nodes to be scored (310 eliminated genes)
##
## Level 13: 205 nodes to be scored (688 eliminated genes)
##
## Level 12: 323 nodes to be scored (1672 eliminated genes)
##
## Level 11: 590 nodes to be scored (3743 eliminated genes)
##
## Level 10: 863 nodes to be scored (5767 eliminated genes)
##
## Level 9: 1050 nodes to be scored (7438 eliminated genes)
##
## Level 8: 1038 nodes to be scored (9383 eliminated genes)
##
## Level 7: 951 nodes to be scored (10899 eliminated genes)
##
## Level 6: 779 nodes to be scored (12083 eliminated genes)
##
## Level 5: 468 nodes to be scored (12775 eliminated genes)
##
## Level 4: 246 nodes to be scored (13262 eliminated genes)
##
## Level 3: 109 nodes to be scored (13445 eliminated genes)
##
## Level 2: 22 nodes to be scored (13535 eliminated genes)
##
## Level 1: 1 nodes to be scored (13586 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/BPUp'), recursive=TRUE)
#GOAnnotation(ResBPUp$ResSel, GOdata=ResBPUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/BPUp'), keytype='SYMBOL')GOTable(ResBPUpHT$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResBPAllHT$ResSel, TopGOResDown=ResBPDownHT$ResSel, TopGOResUp = ResBPUpHT$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResBPAllHT$ResSel, TopGOResDown = ResBPDownHT$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`On the basis of the analysis settings, the enrichment for Molecular Function IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
MFannHT <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(GeneVectors$DEGenes),
mapping='org.Hs.eg.db', ID='symbol') %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResMFAllHT <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=MFannHT, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFAllHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4161 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4624 GO terms and 5978 relations. )
##
## Annotating nodes ...............
## ( 14167 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1479 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 5 nodes to be scored (0 eliminated genes)
##
## Level 11: 19 nodes to be scored (0 eliminated genes)
##
## Level 10: 37 nodes to be scored (36 eliminated genes)
##
## Level 9: 77 nodes to be scored (214 eliminated genes)
##
## Level 8: 143 nodes to be scored (1429 eliminated genes)
##
## Level 7: 249 nodes to be scored (3539 eliminated genes)
##
## Level 6: 307 nodes to be scored (4422 eliminated genes)
##
## Level 5: 320 nodes to be scored (6435 eliminated genes)
##
## Level 4: 238 nodes to be scored (9301 eliminated genes)
##
## Level 3: 67 nodes to be scored (11486 eliminated genes)
##
## Level 2: 16 nodes to be scored (12217 eliminated genes)
##
## Level 1: 1 nodes to be scored (14033 eliminated genes)ResMFDownHT <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFannHT, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFDownHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4161 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4624 GO terms and 5978 relations. )
##
## Annotating nodes ...............
## ( 14167 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1241 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 16 nodes to be scored (0 eliminated genes)
##
## Level 10: 29 nodes to be scored (22 eliminated genes)
##
## Level 9: 58 nodes to be scored (207 eliminated genes)
##
## Level 8: 113 nodes to be scored (1373 eliminated genes)
##
## Level 7: 197 nodes to be scored (3435 eliminated genes)
##
## Level 6: 256 nodes to be scored (4250 eliminated genes)
##
## Level 5: 283 nodes to be scored (6122 eliminated genes)
##
## Level 4: 207 nodes to be scored (9113 eliminated genes)
##
## Level 3: 62 nodes to be scored (11423 eliminated genes)
##
## Level 2: 16 nodes to be scored (12178 eliminated genes)
##
## Level 1: 1 nodes to be scored (14030 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/MFDown'), recursive=TRUE)
#GOAnnotation(ResMFDown$ResSel, GOdata=ResMFDown$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/MFDown'), keytype='SYMBOL')GOTable(ResMFDownHT$ResSel, maxGO=20)ResMFUpHT <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFannHT, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFUpHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4161 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4624 GO terms and 5978 relations. )
##
## Annotating nodes ...............
## ( 14167 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 1215 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 4 nodes to be scored (0 eliminated genes)
##
## Level 11: 15 nodes to be scored (0 eliminated genes)
##
## Level 10: 26 nodes to be scored (30 eliminated genes)
##
## Level 9: 59 nodes to be scored (199 eliminated genes)
##
## Level 8: 108 nodes to be scored (1334 eliminated genes)
##
## Level 7: 199 nodes to be scored (3426 eliminated genes)
##
## Level 6: 260 nodes to be scored (4255 eliminated genes)
##
## Level 5: 262 nodes to be scored (6195 eliminated genes)
##
## Level 4: 207 nodes to be scored (9088 eliminated genes)
##
## Level 3: 58 nodes to be scored (11413 eliminated genes)
##
## Level 2: 16 nodes to be scored (12181 eliminated genes)
##
## Level 1: 1 nodes to be scored (14031 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/MFUp'), recursive=TRUE)
#GOAnnotation(ResMFUp$ResSel, GOdata=ResMFUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/MFUp'), keytype='SYMBOL')GOTable(ResMFUpHT$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResMFAllHT$ResSel, TopGOResDown=ResMFDownHT$ResSel, TopGOResUp=ResMFUpHT$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResMFAllHT$ResSel, TopGOResDown = ResMFDownHT$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
CCannHT <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$DEGenes),
mapping='org.Hs.eg.db', ID='symbol') %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResCCAllHT <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=CCannHT, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCAllHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1701 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1923 GO terms and 3234 relations. )
##
## Annotating nodes ...............
## ( 14409 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 850 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 3 nodes to be scored (0 eliminated genes)
##
## Level 12: 8 nodes to be scored (0 eliminated genes)
##
## Level 11: 38 nodes to be scored (52 eliminated genes)
##
## Level 10: 90 nodes to be scored (103 eliminated genes)
##
## Level 9: 129 nodes to be scored (937 eliminated genes)
##
## Level 8: 130 nodes to be scored (2889 eliminated genes)
##
## Level 7: 137 nodes to be scored (5185 eliminated genes)
##
## Level 6: 118 nodes to be scored (8791 eliminated genes)
##
## Level 5: 89 nodes to be scored (10501 eliminated genes)
##
## Level 4: 58 nodes to be scored (12526 eliminated genes)
##
## Level 3: 47 nodes to be scored (13792 eliminated genes)
##
## Level 2: 2 nodes to be scored (14206 eliminated genes)
##
## Level 1: 1 nodes to be scored (14336 eliminated genes)
#write.table(ResCCAll$ResAll, file=paste0(SavingFolder, 'TopGO/CCAllResults.txt'), sep='\t', row.names=FALSE)# Wrapper function for topGO analysis (see helper file)
ResCCDownHT <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=CCannHT, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCDownHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1701 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1923 GO terms and 3234 relations. )
##
## Annotating nodes ...............
## ( 14409 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 711 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 3 nodes to be scored (0 eliminated genes)
##
## Level 12: 6 nodes to be scored (0 eliminated genes)
##
## Level 11: 32 nodes to be scored (52 eliminated genes)
##
## Level 10: 74 nodes to be scored (85 eliminated genes)
##
## Level 9: 103 nodes to be scored (851 eliminated genes)
##
## Level 8: 105 nodes to be scored (2783 eliminated genes)
##
## Level 7: 112 nodes to be scored (5055 eliminated genes)
##
## Level 6: 105 nodes to be scored (8741 eliminated genes)
##
## Level 5: 74 nodes to be scored (10473 eliminated genes)
##
## Level 4: 51 nodes to be scored (12524 eliminated genes)
##
## Level 3: 43 nodes to be scored (13792 eliminated genes)
##
## Level 2: 2 nodes to be scored (14206 eliminated genes)
##
## Level 1: 1 nodes to be scored (14336 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/CCDown'), recursive=TRUE)
#GOAnnotation(ResCCDown$ResSel, GOdata=ResCCDown$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/CCDown'), keytype='SYMBOL')GOTable(ResCCDownHT$ResSel, maxGO=20)# Wrapper function for topGO analysis (see helper file)
ResCCUpHT <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=CCannHT, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCUpHT', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1701 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1923 GO terms and 3234 relations. )
##
## Annotating nodes ...............
## ( 14409 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 703 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 31 nodes to be scored (0 eliminated genes)
##
## Level 10: 71 nodes to be scored (26 eliminated genes)
##
## Level 9: 104 nodes to be scored (842 eliminated genes)
##
## Level 8: 108 nodes to be scored (2665 eliminated genes)
##
## Level 7: 114 nodes to be scored (5049 eliminated genes)
##
## Level 6: 100 nodes to be scored (8647 eliminated genes)
##
## Level 5: 81 nodes to be scored (10460 eliminated genes)
##
## Level 4: 48 nodes to be scored (12496 eliminated genes)
##
## Level 3: 40 nodes to be scored (13787 eliminated genes)
##
## Level 2: 2 nodes to be scored (14203 eliminated genes)
##
## Level 1: 1 nodes to be scored (14336 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/CCUp'), recursive=TRUE)
#GOAnnotation(ResCCUp$ResSel, GOdata=ResCCUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/CCUp'), keytype='SYMBOL')GOTable(ResCCUpHT$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResCCAllHT$ResSel, TopGOResDown=ResCCDownHT$ResSel, TopGOResUp=ResCCUpHT$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResCCAllHT$ResSel, TopGOResDown = ResCCDownHT$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`Most of the useful information has been saved during the analysis. Here I save figures, workspace and information about the session.
if (params$SaveImages == TRUE){ #Just in case since eval only works when knitting
#Set the folder paths
from <- paste(getwd(), paste(params$NbName, 'files/figure-html', sep='_'), sep='/')
to <- params$FiguresFolder
#Copy to output directory
file.copy(from, to, recursive = TRUE, copy.mode = TRUE)
}SessionInfo <- sessionInfo()
Date <- date()
save.image(paste0(SavingFolder, '/ipsc.', 'FunctionalAnalysisWorkspace_HT.RData'))